
import matplotlib as mpl 
import matplotlib.pyplot as plt
import pandas as pd 
import numpy as np 
mpl.__version__

'3.2.2'


plt.style.use(['seaborn-notebook'])


fig = plt.figure()
ax = plt.axes();  # 축 생성


fig = plt.figure() 
plt.plot([0, 0.2, 0.4, 0.6, 0.8, 1.0] * 5);


x = np.arange(0, 10, 0.01)

fig = plt.figure() 
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x));


plt.plot(np.random.randn(50).cumsum());


plt.plot(np.random.randn(50).cumsum(), linestyle = 'solid')
plt.plot(np.random.randn(50).cumsum(), linestyle = 'dashed');
plt.plot(np.random.randn(50).cumsum(), linestyle = 'dashdot');
plt.plot(np.random.randn(50).cumsum(), linestyle = 'dotted');


plt.plot(np.random.randn(50).cumsum(), color = 'saddlebrown')
plt.plot(np.random.randn(50).cumsum(), color = 'orangered')
plt.plot(np.random.randn(50).cumsum(), color = 'lightpink')
plt.plot(np.random.randn(50).cumsum(), color = 'orchid')

[<matplotlib.lines.Line2D at 0x7f38df926b10>]


plt.plot(np.random.randn(50).cumsum(),'r--')
plt.plot(np.random.randn(50).cumsum(), 'g:')
plt.plot(np.random.randn(50).cumsum(), 'b.-');


plt.plot(np.random.randn(50));
plt.xlim(-1, 50) # x축 범위
plt.ylim(-5, 5); # y축 범위


plt.plot(np.random.randn(50))
plt.axis([-1, 50, -5, 5]);


plt.plot(np.random.randn(50));
plt.axis('tight');


plt.plot(np.random.randn(50));
plt.axis('equal');


plt.plot(np.random.randn(50), label = 'A')
plt.plot(np.random.randn(50), label = 'B')
plt.plot(np.random.randn(50), label = 'C')
plt.title("title") # 제목
plt.xlabel("X") # X축 레이블
plt.ylabel("Y"); # Y축 레이블
plt.legend(); # 범례


set([f.name for f in mpl.font_manager.fontManager.ttflist])

{'DejaVu Sans',
 'DejaVu Sans Display',
 'DejaVu Sans Mono',
 'DejaVu Serif',
 'DejaVu Serif Display',
 'Humor Sans',
 'Liberation Mono',
 'Liberation Sans',
 'Liberation Sans Narrow',
 'Liberation Serif',
 'STIXGeneral',
 'STIXNonUnicode',
 'STIXSizeFiveSym',
 'STIXSizeFourSym',
 'STIXSizeOneSym',
 'STIXSizeThreeSym',
 'STIXSizeTwoSym',
 'cmb10',
 'cmex10',
 'cmmi10',
 'cmr10',
 'cmss10',
 'cmsy10',
 'cmtt10'}


font1 = {'family' : 'Dejavu Sans', 'size' : 24, 'color' : 'black'} 
font2 = {'family' : 'Liberation Mono', 'size' : 24, 'weight' : 'bold', 'color' : 'darkred'} 
font3 = {'family' : 'STIXGeneral', 'size' : 24, 'weight' : 'light', 'color' : 'blue'} 

plt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5])
plt.title('font1', fontdict = font1)
plt.xlabel("font2", fontdict = font2)
plt.ylabel("font3", fontdict = font3);


fig, ax = plt.subplots()
ax.plot(np.random.randn(10), '-r', label = 'A')
ax.plot(np.random.randn(10), ':g', label = 'B')
ax.plot(np.random.randn(10), '--b', label = 'C')
ax.axis('equal')
ax.legend(loc = 'upper center', # 범례 위치
          frameon = False, # 범례상자를 안보이게
          ncol = 3); # 범례 표시를 3열로


ax.legend(fancybox = True, framealpha = 1, # 범례의 투명도 
          shadow = True, # 범례의 그림자
          borderpad=1) # 범례에 여백
fig


plt.figure(figsize = (8, 4))
x = np.linspace(0, 10, 1000)
y = np.cos(x[:, np.newaxis] * np.arange(0, 2, 0.2))
lines = plt.plot(x, y)
plt.legend(lines[:3], ['c1', 'c2', 'c3']);


plt.plot(x, y[:, 0], label = 'c1')
plt.plot(x, y[:, 1], label = 'c2')
plt.plot(x, y[:, 2], label = 'c3')
plt.plot(x, y[:, 3])
plt.legend(framealpha = 1, frameon = False, # True로 
           loc = 'upper right');


x = np.linspace(0, 20, 100)
I = np.cos(x) - np.cos(x[:, np.newaxis])

plt.imshow(I)
plt.colorbar();


plt.imshow(I, cmap = 'Blues')
plt.colorbar();


ax1 = plt.axes()
ax2 = plt.axes([0.65, 0.5, 0.2, 0.3]);


for i in range(1,10):
  plt.subplot(3, 3, i)
  plt.text(0.5, 0.5, str((3, 3, i)), ha = 'center') # 다중 플롯이 생성됬으나 겹침


fig = plt.figure()
fig.subplots_adjust(hspace = 0.4, # 플롯간 세로 간격
                    wspace = 0.4) # 플롯간 가로 간격
for i in range(1,10):
  plt.subplot(3, 3, i)
  plt.text(0.5, 0.5, str((3, 3, i)), ha = 'center')


fig, ax = plt.subplots(3, 3, sharex = 'col', sharey = 'row')


for i in range(3):
  for j in range(3):
    ax[i, j].text(0.5, 0.5, str((i, j)), ha = 'center')
fig


grid = plt.GridSpec(2, 3, wspace = 0.4, hspace = 0.4)

plt.subplot(grid[0, 0])
plt.subplot(grid[0, 1:])
plt.subplot(grid[1, :2])
plt.subplot(grid[1, 2])

<matplotlib.axes._subplots.AxesSubplot at 0x7f38dbe10f50>


plt.figure(figsize = (5, 6))

x = range(1, 21)
columns = [np.random.randn(20) * i for i in range(1, 7)]

i = 0
for c in columns:
  i += 1

  plt.subplot(3, 2, i)
  plt.plot(x, c, marker = 'o', linewidth = 1, label = c)
  plt.xlim(-1, 21)
  plt.ylim(c.min()-1, c.max()+1)


fig, ax = plt.subplots()
ax.axis([0, 10, 0, 10])
ax.text(3, 6, ". transData(3, 6)", transform = ax.transData) # 좌표에 텍스트 표시
ax.text(0.2, 0.4, ". transAxes(0.2, 0.4)", transform = ax.transAxes) # 각 축의 비율에 해당하는 위치
ax.text(0.2, 0.4, ". transFigure(0.2, 0.2)", transform = fig.transFigure); # 플롯 전체의 비율에 해당하는 위치


x = np.arange(1, 40)
y = x * 1.1
plt.scatter(x, y, marker = '.')
plt.axis('equal')
plt.annotate('interesting plot', 
             xy = (4, 5), # 화살표로 가리킬 위치
             xytext = (20, 10), # 텍스트의 위치
             arrowprops = dict(shrink = 0.05)); # 화살표의 두께


x1 = np.random.normal(30, 3, 100)
x2 = np.random.normal(30, 3, 100)
x3 = np.random.normal(30, 3, 100)

plt.plot(x1, label = 'p1')
plt.plot(x1, label = 'p2')
plt.plot(x1, label = 'p3')

plt.legend(bbox_to_anchor = (0., 1.02, 1., .102), loc = 0, ncol = 3,
           mode = 'extend', borderaxespad = 0.)
plt.annotate('important value', (30, 30), xytext = (50, 40), arrowprops = dict(arrowstyle = '->'))
plt.annotate('incorrect value', (30, 30), xytext = (30, 40), arrowprops = dict(arrowstyle = '->'));


plt.axes(xscale = 'log', yscale = 'log');


ax = plt.axes()
ax.plot(np.random.randn(100).cumsum())

ax.yaxis.set_major_locator(plt.NullLocator()) # y축의 정보 삭제
ax.xaxis.set_major_formatter(plt.NullFormatter()) # x축의 눈금만 놔두고 삭제


fig, ax = plt.subplots(3, 3, sharex = True, sharey = True); # X축과 Y축을 공유


for ax1 in ax.flat:
  ax1.xaxis.set_major_locator(plt.MaxNLocator(4))
  ax1.yaxis.set_major_locator(plt.MaxNLocator(4))
fig


x = np.linspace(-np.pi, np.pi, 1000, endpoint = True)
y = np.sin(x)
plt.plot(x, y)

ax = plt.gca()
ax.spines['bottom'].set_position(('data', 0)) # 0을 기준으로 위쪽에 선
ax.spines['left'].set_position(('data', 0)) # 0을 기준으로 왼쪽에 선
ax.xaxis.set_ticks_position('bottom') # x축의 정보를 축 아래에
ax.yaxis.set_ticks_position('left') # y축의 정보를 축 왼쪽에


x = np.linspace(1, 10)
y = [10 ** el for el in x]
z = [2 * el for el in x]

fig = plt.figure(figsize = (10, 8))

ax1 = fig.add_subplot(2, 2, 1)
ax1.plot(x, y, '-y')
ax1.set_yscale('log')
ax1.set_title('Logarithmic plot of $ {10}^{x} $')
ax1.set_ylabel(r'$ {y} = {10}^{x} $')
plt.grid(b = True, which = 'both', axis = 'both')

ax1 = fig.add_subplot(2, 2, 2)
ax1.plot(x, y, '-r')
ax1.set_yscale('linear')
ax1.set_title('Linear plot of $ {10}^{x} $')
ax1.set_ylabel(r'$ {y} = {10}^{x} $')
plt.grid(b = True, which = 'both', axis = 'both')

ax1 = fig.add_subplot(2, 2, 3)
ax1.plot(x, z, '-g')
ax1.set_yscale('log')
ax1.set_title('Logarithmic plot of $ {2}^{x} $')
ax1.set_ylabel(r'$ {y} = {10}^{x} $')
plt.grid(b = True, which = 'both', axis = 'both')

ax1 = fig.add_subplot(2, 2, 4)
ax1.plot(x, z, '-b')
ax1.set_yscale('linear')
ax1.set_title('Linear plot of $ {2}^{x} $')
ax1.set_ylabel(r'$ {y} = {10}^{x} $')
plt.grid(b = True, which = 'both', axis = 'both')


fig = plt.figure(figsize = (10, 10))
x = range(1, 11)
columns = [np.random.randn(10) * i for i in range(1, 26)]

for n, v in enumerate(plt.style.available[1:]):
  plt.style.use(v)
  plt.subplot(5, 5, n + 1)
  plt.title(v)

  for c in columns:
    plt.plot(x, c, marker = '', color = 'royalblue', linewidth = 1, alpha = 0.1)
    plt.subplots_adjust(hspace = 0.5, wspace = 0.4)


height = [np.random.randn() * i for i in range(1, 6)]
names = ['A', 'B', 'C', 'D', 'E']
y_pos = np.arange(len(names)) 
plt.bar(y_pos, height) 
plt.xticks(y_pos, names, fontweight = 'bold')
plt.xlabel('group');


# 세로 막대그래프 
height = [np.random.randn() * i for i in range(1, 6)]
names = ['A', 'B', 'C', 'D', 'E']
y_pos = np.arange(len(names)) 
plt.barh(y_pos, height) 
plt.yticks(y_pos, names, fontweight = 'bold')
plt.ylabel('group');


bars1 = [12, 28, 1, 8, 22]
bars2 = [28, 7, 16, 4, 10]
bars3 = [25, 3, 23, 25, 17]
bars = np.add(bars1, bars2).tolist()

r = [0, 1, 2, 3, 4]
names = ['A', 'B', 'C', 'D', 'E']

plt.bar(r, bars1, color = 'royalblue', edgecolor = 'white')
plt.bar(r, bars2, bottom = bars1, color = 'skyblue', edgecolor = 'white')
plt.bar(r, bars3, bottom = bars2, color = 'lightblue', edgecolor = 'white')

plt.xlabel('groups', fontweight = 'bold')
plt.xticks(r, names, fontweight = 'bold');


bar_width = 0.25

bars1 = [14, 17, 9, 8, 7]
bars2 = [14, 6, 16, 4, 10]
bars3 = [21, 4, 23, 13, 17]

r1 = np.arange(len(bars1))
r2 = [x + bar_width for x in r1]
r3 = [x + bar_width for x in r2]

plt.bar(r1, bars1, color = 'royalblue', width = bar_width, edgecolor = 'white', label = 'r1')
plt.bar(r2, bars2, color = 'skyblue',  width = bar_width, edgecolor = 'white', label = 'r2')
plt.bar(r3, bars3, color = 'lightblue', width = bar_width, edgecolor = 'white', label = 'r3')

r = [0, 1, 2, 3, 4]
plt.xlabel('groups', fontweight = 'bold')
plt.xticks([r + bar_width for r in range(len(bars1))], ['A', 'B', 'C', 'D', 'E'])
plt.legend();


x = np.linspace(0.1,  2 * np.pi, 41)
y = np.exp(np.sin(x))

plt.stem(x, y, linefmt = 'gray', bottom = 1, # Y기준 1의 위치에 bottom선
         use_line_collection = True)

<StemContainer object of 3 artists>


r1 = np.random.normal(loc = 0, scale = 0.5, size = 100)
r2 = np.random.normal(loc = 0.5, scale = 1, size = 100)
r3 = np.random.normal(loc = 1, scale = 1.5, size = 100)
r4 = np.random.normal(loc = 1.5, scale = 2, size = 100)
r5 = np.random.normal(loc = 2, scale = 2.5, size = 100)

fig, ax = plt.subplots(1, 1) 
ax.boxplot((r1, r2, r3, r4, r5))
ax.set_xticklabels(['r1', 'r2', 'r3', 'r4', 'r5']);


plt.figure(figsize = (8, 4))
markers = ['.', ',', 'o', 'v', '^', '<', '>', '1', '2', '3', '4', 's', 'p', '*', 'h', 'H', '+', 'D', 'd']
for m in markers:
  plt.plot(np.random.rand(5), np.random.rand(5),  m, label = m)

plt.legend(loc = 'center right', ncol = 2)
plt.xlim(0, 1.5);


for i in range(9) :
  x = np.arange(1000)
  y = np.random.randn(1000).cumsum()
  plt.scatter(x, y, alpha = 0.2, cmap = 'viridis')


x = np.random.randn(100)
y = np.random.randn(100)
colors = np.random.randn(100)
sizes = 1000 * np.random.randn(100)

plt.scatter(x, y, c = colors , s = sizes, alpha = 0.3, cmap = 'CMRmap')
plt.colorbar();

/usr/local/lib/python3.7/dist-packages/matplotlib/collections.py:885: RuntimeWarning: invalid value encountered in sqrt
  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor


plt.subplots_adjust(wspace = 1)

dt = 0.01
t = np.arange(0, 30, dt)
n1 = np.random.randn(len(t))
n2 = np.random.randn(len(t))
r = np.exp(-t/0.05)

c1 = np.convolve(n1, r, mode = 'same') * dt
c2 = np.convolve(n2, r, mode = 'same') * dt

s1 = 0.01 * np.sin(2 * np.pi * 10 * t) + c1
s2 = 0.01 * np.sin(2 * np.pi * 10 * t) + c2

plt.subplot(211)
plt.plot(t, s1, t, s2)
plt.xlim(0, 5)
plt.xlabel('time')
plt.ylabel('s1 & s2')
plt.grid(True)

plt.subplot(212)
plt.cohere(s1, s2 ** 2, 256, 1./dt)
plt.ylabel('coherence');


x = np.linspace(0, 20, 40)
dy = 1 
y = np.sin(x) + dy * np.random.randn(40)

plt.errorbar(x, y, yerr=dy, fmt = 's', color = 'darkblue', # 점의 색상
             ecolor = 'gray', # 선의 색상
             elinewidth = 5,  # 선의 두께
             capsize = 0);


plt.pcolor(np.random.rand(20, 40), cmap = 'Reds');


data = np.random.randn(10000)
plt.hist(data);


plt.hist(data, bins = 50, # 구간을 50개
         alpha = 0.5, # 투명도
         histtype = 'stepfilled',
         color = 'steelblue',
         edgecolor = 'navy');


x1 = np.random.normal(0, 1, 10000)
x2 = np.random.normal(-5, 3, 10000)
x3 = np.random.normal(5, 2, 10000)
d = dict(histtype = 'stepfilled', alpha = 0.3, bins = 50)

plt.hist(x1, **d)
plt.hist(x2, **d)
plt.hist(x3, **d);


x = np.random.normal(size = 50000)
y = x - np.random.normal(size = 50000) 

plt.hist2d(x, y, bins = 50, cmap = 'OrRd')
plt.colorbar();


x = np.random.normal(size = 50000)
y = x - np.random.normal(size = 50000) 

plt.hexbin(x, y, gridsize = 20, cmap = 'OrRd')
plt.colorbar();


a = np.arange(-1, 1, 0.1)
X, Y = np.meshgrid(a, a)

Z = np.sin(X * Y)
CS = plt.contour(X, Y, Z, levels = a) 
plt.clabel(CS, inline = 2)
plt.colorbar();


def f(x, y) :
  return (1 - (x ** 2 + y **2)) + np.exp(-y ** 2 / 2)

x = np.arange(-1.5, 1.5, 0.1)
y = np.arange(-1.5, 1.5, 0.1)

X, Y = np.meshgrid(x, y)
Z = f(X, Y)
N = np.arange(-1, 2, 0.2)

CS = plt.contour(Z, N, linewidth = 2, cmap = 'rainbow')
plt.clabel(CS, inline = True, fmt = '%1.1f')
plt.colorbar(CS);

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: UserWarning: The following kwargs were not used by contour: 'linewidth'
  # This is added back by InteractiveShellApp.init_path()


plt.contourf(X, Y, Z)
plt.colorbar();


plt.imshow(Z, extent = [-1, 1, -1, 1], origin = 'lower', cmap = 'rainbow', alpha = 0.4);


Y, X = np.mgrid[0:5:100j, 0:5:100j]
U = X 
V = np.sin(Y) 
plt.streamplot(X, Y, U, V);


import sympy  

x, y = sympy.symbols('x y')
f = x**2 + y**2 + x*y - sympy.sin(x) * 4
fdx = sympy.diff(f, x)
fdy = sympy.diff(f, y) 

sample_size = 100 
xs, ys = np.meshgrid(np.linspace(-10, 10, sample_size), np.linspace(-10, 10, sample_size)) 

zs = [float(f.subs(x, xv).subs(y, yv)) for xv, yv in zip(xs.ravel(), ys.ravel())]
zs = np.array(zs).reshape(sample_size, sample_size) 

plt.contour(xs, ys, zs, 40, levels = np.logspace(-0.5, 2.0, 40), cmap = 'rainbow')

xs_q, ys_q = np.meshgrid(np.linspace(-10, 10, 10), np.linspace(-10, 10, 10))

xsg = [-float(fdx.subs(x, xv).subs(y, yv)) for xv, yv in zip(xs_q.ravel(), ys_q.ravel())]
ysg = [-float(fdx.subs(x, xv).subs(y, yv)) for xv, yv in zip(xs_q.ravel(), ys_q.ravel())]

plt.quiver(xs_q, ys_q, xsg, ysg, width = 0.005, scale = 500, color = 'black');


data = [10, 50, 30, 40, 60]
categories = ['C1', 'C2', 'C3', 'C4', 'C5'] 
plt.pie(data, labels = categories, autopct = '%0.1f%%')
plt.legend();


data = [10, 50, 30, 40, 60]
categories = ['C1', 'C2', 'C3', 'C4', 'C5'] 
explode = [0.1] * 5
plt.pie(data, explode = explode, labels = categories, autopct = '%0.1f%%')
plt.legend();


df = pd.DataFrame({
    'group' : ['A', 'B', 'C', 'D'],
    'var1' : [38, 1.5, 30, 4],
    'var2' : [29, 10, 9, 34],
    'var3' : [8, 39, 23, 24],
    'var4' : [7, 31, 33, 14],
    'var5' : [28, 15, 32, 14]
})

categories = list(df)[1:]
N = len(categories) 

angles = [n / float(N) * 2 * np.pi for n in range(N)]
angles += angles[:1]

ax = plt.subplot(111, polar = True)
ax.set_theta_offset(np.pi / 2)
ax.set_theta_direction(-1) 
ax.set_rlabel_position(0)
plt.xticks(angles[:-1], categories)
plt.yticks([10, 20, 30], ["10", "20", "30"], color = 'gray', size = 7)
plt.ylim(0, 40) 

values = df.loc[0].drop('group').values.flatten().tolist()
values += values[:1]
ax.plot(angles, values, linewidth = 1, linestyle = 'solid', label = 'A') 
ax.fill(angles, values, 'b', alpha = 0.1)

values = df.loc[1].drop('group').values.flatten().tolist() 
values += values[:1]
ax.plot(angles, values, linewidth = 1, linestyle = 'solid', label = 'B')
ax.fill(angles, values, 'r', alpha = 0.1)

plt.legend(bbox_to_anchor = (0.1, 0.1));


from matplotlib.sankey import Sankey

Sankey(flows = [0.20, 0.15, 0.25, -0.25, -0.25, -0.15, -0.60, -0.20],
       labels = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven'],
       orientations = [-1, -1, 0, 1, 1, 1, 0, -1]).finish(); # -1은 밑으로, 1은 위로, 0은 오른쪽


from mpl_toolkits import mplot3d


fig = plt.figure()
ax = plt.axes(projection = '3d')


x = range(1, 101)
y = np.random.randn(100) * x
z = np.random.randn(100) * x

fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.scatter(x, y, z, c = 'green', s = 60);


ax = plt.axes(projection = '3d') 

zline = np.linspace(0, 20, 1000)
xline = np.sin(zline)  
yline = np.cos(zline) 
ax.plot3D(xline, yline, zline, c = 'gray') 

zdata = 20 * np.random.random(100)  
xdata = np.sin(zdata) + 0.2 * np.random.randn(100)
ydata = np.cos(zdata) + 0.2 * np.random.randn(100) 
ax.scatter3D(xdata, ydata, zdata, c = zdata, cmap = 'Blues');


def f(x, y) :
  return np.cos(np.sqrt(x**2 + y**2))

l = np.linspace(-4, 4, 20) 
X, Y = np.meshgrid(l, l)
Z = f(X, Y)


fig = plt.figure()  
ax = plt.axes(projection = '3d')
ax.contour3D(X, Y, Z, 50, cmap = 'BuPu')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')

/usr/local/lib/python3.7/dist-packages/mpl_toolkits/mplot3d/art3d.py:304: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  self._segments3d = np.asanyarray(segments)

Text(0.5, 0, 'z')


ax.view_init(60, 30)  
fig


fig = plt.figure() 
ax = plt.axes(projection = '3d')  
ax.plot_wireframe(X, Y, Z, color = 'gray');


ax = plt.axes(projection = '3d')    
ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, 
                cmap = 'viridis', edgecolor = 'none')

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f38d9748110>


r = np.linspace(0, 6, 20) 
theta = np.linspace(-0.8 * np.pi, 0.8 * np.pi, 40)
r, theta = np.meshgrid(r, theta) 

X = r * np.sin(theta)
Y = r * np.cos(theta)
Z = f(X, Y) 

ax = plt.axes(projection = '3d')
ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, 
                cmap = 'viridis', edgecolor = 'none')

<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f38da1cd490>


theta = 2 * np.pi * np.random.random(1000) 
r = 6 * np.random.random(1000)
X = np.ravel(r * np.sin(theta))
Y = np.ravel(r * np.cos(theta)) 
Z = f(X, Y)

ax = plt.axes(projection = '3d')  
ax.scatter(X, Y, Z, c = Z, cmap = 'viridis', linewidth = 0.5);


ax = plt.axes(projection = '3d')  
ax.plot_trisurf(X, Y, Z, cmap = 'viridis', edgecolor = 'none');

함수	설명
`bar`	막대 플롯(bar plot) 생성
`barbs`	barbs의 2차원 필드 그리기
`boxplot`	상자 및 수염 플롯 생성
`cohere`	x와 y의 일관성 시각화 그리기
`contour`	플롯 등고선
`errorbar`	오류 막대 그래프
`hexbin`	육각형 binning 플롯 생성
`hist`	히스토그램 플롯
`imshow`	축에 이미지 표시
`pcolor`	2차원 배열의 유사 플롯 생성
`pcolormesh`	사각 망사 그래프
`pie`	파이 차트 플롯
`plot`	플롯 라인 또는 마커
`quiver`	화살표의 2차원 필드 생성
`sankey`	Sankey 흐름도 생성
`scatter`	x대 y의 산점도 생성
`stem`	줄기 그래프 생성
`streamplot`	벡터 흐름의 스트림 라인 그리기

Matplotlib¶

Matplotlib 특징¶

라인 플롯(Line Plot)¶

라인 스타일(Line Style)¶

색상 스타일(Color Style)¶

플롯 축(Plot Axis)¶

플롯 레이블(Plot Label)¶

폰트 관리자(Font Manager)¶

플롯 범례(Plot Legend)¶

다중 플롯(Multiple Subplots)¶

텍스트와 주석(Text and Annotation)¶

눈금 맞춤(Customizing Ticks)¶

스타일(Style)¶

플롯 종류¶

막대 플롯(Barplot)¶

스템 플롯(Stem Plot)¶

박스플롯(Box Plot)¶

산점도(Scatter Plot)¶

버블차트¶

x와 y의 일관성 차트(coherence)¶

오차 막대(Error Bar)¶

2차원 유사 플롯, 히트맵 (pcolor/pcolormesh)¶

히스토그램, 구간화, 밀도(Histograms, Binnings, and Density)¶

밀도와 등고선 플롯(Density and Contour Plots)¶

스트림 플롯(Stream Plot)¶

화살표 2차원 필드(quiver)¶

파이 차트(Pie Chart)¶

레이다 차트, 스타 차트(Radar Chart)¶

생키 다이어그램(Sankey Diagram)¶

3차원 플로팅(Three-Dimensional Plotting)¶

참고문헌¶